Recognition Marker Research Articles

Targeting of biomolecular condensates to the autophagy pathway.

Biomolecular condensates are membraneless compartments formed by liquid-liquid phase separation. They can phase transit into gel-like and solid states. The amount and state of biomolecular condensates must be tightly regulated to maintain normal cellular function. Autophagy targets biomolecular condensates to the lysosome for degradation or other purposes, which we term biocondensophagy. In biocondensophagy, autophagy receptors recognize biomolecular condensates and target them to the autophagosome, the vesicle carrier of autophagy. Multiple types of autophagy receptors have been identified and they are specifically involved in targeting biomolecular condensates with different phase transition states. The receptors also organize the phase transition of biomolecular condensate to facilitate biocondensophagy. Here, we briefly discuss the latest discoveries regarding how biomolecular condensates are recognized by autophagy receptors.

Discrimination of Green Coffee (Coffea arabica and Coffea canephora) of Different Geographical Origin Based on Antioxidant Activity, High-Throughput Metabolomics, and DNA RFLP Fingerprinting.

The genus Coffea is known for the two species C. arabica (CA) and C. canephora (CC), which are used to prepare the beverage coffee. Proper identification of green beans of coffee varieties is based on phenotypic and phytochemical/molecular characteristics. In this work, a combination of chemical (UV/Vis, HPLC-DAD-MS/MS, GC-MS, and GC-FID) and molecular (PCR-RFLP) fingerprinting was used to discriminate commercial green coffee accessions from different geographical origin. The highest content of polyphenols and flavonoids was always found in CC accessions, whereas CA showed lower values. ABTS and FRAP assays showed a significant correlation between phenolic content and antioxidant activity in most CC accessions. We identified 32 different compounds, including 28 flavonoids and four N-containing compounds. The highest contents of caffeine and melatonin were detected in CC accessions, whereas the highest levels of quercetin and kaempferol derivatives were found in CA accessions. Fatty acids of CC accessions were characterized by low levels of linoleic and cis octadecenoic acid and high amounts of elaidic acid and myristic acid. Discrimination of species according to their geographical origin was achieved using high-throughput data analysis, combining all measured parameters. Lastly, PCR-RFLP analysis was instrumental for the identification of recognition markers for the majority of accessions. Using the restriction enzyme AluI on the trnL-trnF region, we clearly discriminated C. canephora from C. arabica, whereas the cleavage performed by the restriction enzymes MseI and XholI on the 5S-rRNA-NTS region produced specific discrimination patterns useful for the correct identification of the different coffee accessions. This work extends our previous studies and provides new information on the complete flavonoid profile, combining high-throughput data with DNA fingerprinting to assess the geographical discrimination of green coffee.

Biochemical and molecular depictions to develop ech42 gene-specific SCAR markers for recognition of chitinolytic Trichoderma inhibiting Macrophomina phaseolina (Maubl.) Ashby

Trichoderma isolates were inhibited variably in-vitro growth of soil-borne phytopathogen Macrophomina phaseolina (Maubl.) Ashby causes root rot in cotton. The growth inhibition of test-pathogen was found to be higher (90.36%) in T. viride NBAIITv23 followed by T. koningii MTCC796 (85.77%) under dual culture antagonism. The microscopic examination suggested that the antagonists Tv23 and MTCC796 adopted mycoparasitism as a strong mode of action to restrain pathogen growth. However, antagonists T. harzianum NBAIITh1 (77.89%) and T. virens NBAIITvs12 (61.74%) demonstrated strong antibiosis action for growth inhibition of the test pathogen. A significant positive correlation was established between the growth inhibition of M. phaseolina and the release of cell wall degrading enzymes- chitinase (p = 0.001), β-1,3, glucanase (p = 0.01), and protease (p = 0.05) under the influence of pathogen cell wall. The chitinase and β-1,3, glucanase activities were elevated 2.09 and 1.75 folds, respectively, in potent mycoparasitic Tv23 strain influenced by a pathogen cell wall compared to glucose as a carbon source. The three unique DNA-RAPD fragments OPA-07(1033), OPA-16(983), and OPO-15(239), amplified by potent mycoparasitic Tv23 strain, were subjected to DNA sequencing and derived functional 864bp from OPA-16(983) and have sequence homology to ech42 gene with partial CDs of 262 amino acids (nucleotide accession No. KF723016.1 and protein accession No.AHF57046.1). Novel SCAR markers were developed from a functional sequence of OPA-16 fragments and validated across the genomic DNA of eleven Trichoderma antagonists. The novel SCAR markers evolved from the RAPD-SCAR interface to authenticate chitinolytic Trichoderma associated with mycoparasitic action for eco-friendly biocontrol activity.

Is Serum Human Epididymis Protein HE4, More Specific than Cancer Antigen 125 in Patients with Benign Gynecological Diseases?

Background: The cancer antigen 125 “CA125” and Human Epididymis protein 4 “HE4” are among some well worked out markers for recognition of malignancy in ovarian tumors. Their different cutoffs are determined and reported by various studies and mostly used recommended cutoffs are 35.0 U/mL for CA125 and 140pmol/L for HE4. They are both reported to recognize the malignancy well, but how specific they are for benign gynecological diseases is still needed to be worked out. Aim: To determine and compare the performance of CA125 and HE4 for benign gynecological pathologies. Methodology: A cross-sectional study was performed at Gajju Khan Medical College in collaboration with Armed forces Institute of pathology (AFIP). A total of 76 women of age above 18 years age, with benign ovarian pathology, confirmed through histological evaluation were enrolled. All these were examined on both markers, i.e. CA125 and HE4. Standard cutoffs of CA125 >35.0 U/mL and HE4>140 pmol/L were used to see if the case was labeled malignant, incorrectly. SPSS 20.0 was used to manage and analyze the data. Performance of the two markers were compared by using some descriptive analysis and using chi-square test. P-value ≤0.05 was considered significant. Results: The median age of these women was 40 years ranging between 18 and 71. Majority (86.8%) was premenopausal, The CA125 had no difference between pre and post-menopausal women but HE4 had a significant difference with p-value 0.029. there were 10.5% incorrect labeling of malignancy when evaluated for CA125, while only 2.6% incorrect on HE4. This difference was found statistically significant between two markers with p-value 0.05. Conclusion: HE4 is better and more specific marker for benign gynecological pathologies as compared to CA125 Keywords:Benign Ovarian Epithelial, Human epididymisprotein (HE4), Cancer Antigen 125 (CA125), Specificity

EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search

Objective. Major depressive disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings’ mental activities and can be served as an objective biomarker for diagnosing MDD. Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels. Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition. Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.

Frequency-dependent Microstate Characteristics for Mild Cognitive Impairment in Parkinson's Disease.

Cognitive impairment is typically reflected in the time and frequency variations of electroencephalography (EeG). Integrating time-domain and frequency-domain analysis methods is essential to better understand and assess cognitive ability. Timely identification of cognitive levels in early Parkinson's disease (ePD) patients can help mitigate the risk of future dementia. For the investigation of the brain activity and states related to cognitive levels, this study recruited forty ePD patients for EEG microstate analysis, including 13 with mild cognitive impairment (MCI) and 27 without MCI (control group). To determine the specific frequency band on which the microstate analysis relies, a deep learning framework was employed to discern the frequency dependence of the cognitive level in ePD patients. The input to the convolutional neural network consisted of the power spectral density of multi-channel multi-point EEG signals. The visualization technique of gradient-weighted class activation mapping was utilized to extract the optimal frequency band for identifying MCI samples. Within this frequency band, microstate analysis was conducted and correlated with the Montreal Cognitive Assessment (MoCA) Scale. The deep neural network revealed significant differences in the 1-11.5Hz spectrum of the ePD-MCI group compared to the control group. In this characteristic frequency band, ePD-MCI patients exhibited a pattern of global microstate disorder. The coverage rate and occurrence frequency of microstate A and D increased significantly and were both negatively correlated with the MoCA scale. Meanwhile, the coverage, frequency and duration of microstate C decreased significantly and were positively correlated with the MoCA scale. Our work unveils abnormal microstate characteristics in ePD-MCI based on time-frequency fusion, enhancing our understanding of cognitively related brain dynamics and providing electrophysiological markers for ePD-MCI recognition.

Cell-Surface Autoantibody Targets Zinc Transporter-8 (ZnT8) for InVivo β-Cell Imaging and Islet-Specific Therapies.

Type 1 diabetes (T1D) is a disease in which autoimmune attacks are directed at the insulin-producing β-cell in the pancreatic islet. Autoantigens on the β-cell surface membrane are specific markers for molecular recognition and targets for engagement by autoreactive B lymphocytes, which produce islet cell surface autoantibody (ICSA) upon activation. We report the cloning of an ICSA (mAb43) that recognizes a major T1D autoantigen, ZnT8, with a subnanomolar binding affinity and conformation specificity. We demonstrate that cell-surface binding of mAb43 protects the extracellular epitope of ZnT8 against immunolabeling by serum ICSA from a patient with T1D. Furthermore, mAb43 exhibits invitro and ex vivo specificity for islet cells, mirroring the exquisite specificity of islet autoimmunity in T1D. Systemic administration of mAb43 yields a pancreas-specific biodistribution in mice and islet homing of an mAb43-linked imaging payload through the pancreatic vasculature, thereby validating the invivo specificity of mAb43. Identifying ZnT8 as a major antigenic target of ICSA allows for research into the molecular recognition and engagement of autoreactive B cells in the chronic phase of T1D progression. The invivo islet specificity of mAb43 could be further exploited to develop invivo imaging and islet-specific immunotherapies.

Glymphatic Dysfunction Mediates the Influence of White Matter Hyperintensities on Episodic Memory in Cerebral Small Vessel Disease.

Glymphatic dysfunction has been linked to cognitive decline in several neurodegenerative diseases. In cerebral small vessel disease (CSVD), the mechanism of white matter hyperintensities (WMH)-related cognitive impairment (CI) is still under investigation. The diffusion tensor image (DTI) analysis along the perivascular space (ALPS) method has been considered to be a reliable parameter to evaluate glymphatic function. Therefore, we applied the ALPS-index to determine the influence of glymphatic function on CI in CSVD. In total, 137 CSVD patients (normal cognitive group, mild CI group, and dementia group) and 52 normal controls were included in this study. The ALPS-index was calculated based on the DTI. Correlation analyses and mediation analysis were conducted to examine the relationship between glymphatic function and cognition. Remarkable differences in the ALPS-index were observed between subjects with and without CI. The ALPS-index was negatively correlated with age, WMH volume, and general cognitive function in all CSVD patients. In the mild CI group, the ALPS-index was independently positively related to episodic memory, and mediated the relationship between WMH volume and episodic memory. In conclusion, the ALPS-index is a potential marker for early recognition of CI in CSVD. Glymphatic dysfunction mediates the relationship between WMH and CI in CSVD.

Прогнозування і профілактика прееклампсії при багатоплідній вагітності після допоміжних репродуктивних технологій

Purpose - to assess the effectiveness of the implemented algorithm of treatment and preventive measures by analyzing the course of pregnancy, childbirth and the condition of newborns in multiple pregnancies after assisted reproductive technologies to assess of changes in biochemical markers of endothelial dysfunction in the detection of pre-clinical manifestations of preeclampsia in pregnant women with multiple pregnancies after the use of assisted reproductive technologies. Materials and methods. A prospective study of pregnant women with dichorionic diamniotic twins after assisted reproductive technologies was conducted: the Group І (main) - 35 pregnant women with twins who were offered a developed monitoring algorithm; the Group ІІ (control) 27 pregnant women who were monitored according to generally accepted methods of managing multiple pregnancies. Statistical processing of the research results was carried out using standard programs “Microsoft Excel 5.0” and “Statistica 6.0”. Results. In the Group І 2 cases of moderate preeclampsia were found in 32 and 33 weeks of pregnancy (PLGF results - 63.2 pg/ml and 58.7 pg/ml, sFlt-1/PLGF ratio - 51.7 and 66.3). In the Group II 4 cases of preeclampsia were identified: 2 cases of moderate preeclampsia (PLGF - 74.1 pg/ml and 69.3 pg/ml, sFlt-1/PLGF ratio - 98.6 and 104.5); 2 cases of severe preeclampsia at 33 and 34 weeks (PLGF data - 44.1 pg/ml and 47.3 pg/ml, sFlt-1/ PLGF ratio 122.4 and 130.1) against the background of signs of gestational hypertension. Fetal growth retardation in the Group І was noted in 3 (8.6%) women (І stage - in 2 (5.7%) cases, II stage - in 1 (2.9%) case, III stage was not diagnosed), in the Group II - in 7 (25.9%) women (I stage - in 2 (7.4%) cases, II stage - in 4 (14.8%) cases, III stage - in 1 (3.7%) case). Fetal distress in the Group І was detected in 4 (11.4%) pregnant women (during pregnancy - in 1 (2.9%) case, during deliveries - in 3 (8.6%) cases); in the Group II - in 7 (25.9%) women (during pregnancy - in 5 (18.5%) cases, during deliveries - in 2 (7.4%) cases). Premature deliveries in the Group I was noted in 5 (14.3%) women (before 34 weeks of gestation - in 2 (5.7%) cases, after 34 weeks of gestation - in 3 (8.6%) cases); in the Group II - in 8 (29.6%) women (before 34 weeks of gestation - in 5 (18.5%) cases, after 34 weeks of gestation - in 3 (11.1%) cases). Premature detachment of a normally located placenta was detected in 2 (7.4%) women in the Group II, and in the Group I such cases were not recorded. Vaginal delivery was observed in 14 (40%) women in the Group I and 7 (25.9%) patients in the Group II; cesarean section - in 21 (60%) and 19 (70.4%) women, respectively. Combined delivery was performed in 1 (3.7%) woman of the Group II, and in the Group I - not recorded. Hypoxic-ischemic lesions were detected in 2 (5.7%) children from women of the Group I and in 4 (14.8%) children from mothers of the Group II. Adaptation disorders were noted in 5 (14.3%) cases in the Group I and in 8 (29.6%) cases in the Group II. Transfer to the second stage of treatment required 3 (8.6%) children from mothers of the Group I and 6 (22.2%) children from mothers of the Group II. Conclusions. The proposed algorithm of modern diagnostic and treatment-prophylactic measures made it possible to reliably reduce the frequency of development of preeclampsia, obstetric and perinatal complications and can be recommended for use in practical health care. The study of the anti-angiogenic factor sFlt-1 and the pro-angiogenic factor PLGF and their ratio is promising in the prediction and early diagnosis of preeclampsia in multiple pregnancies after assisted reproductive technologies. The use of Triage PLGF and sFlt-1/PLGF ratio should be widely implemented in the practice of obstetrician-gynecologists as a prognostic marker for early recognition of preeclampsia in order to reduce perinatal complications, both from the mother and the fetus. Based on monitoring the growth dynamics of the sFlt-1/PLGF ratio, it is possible to predict the early development of preeclampsia even without clinical symptoms, and to make a decision about the possibility of extending the pregnancy or the need for urgent delivery. The research was carried out in accordance with the principles of the Helsinki Declaration. The study protocol was approved by the Local Ethics Committee of the participating institution. The informed consent of the patient was obtained for conducting the studies. No conflict of interests was declared by the author.

Resting-state Brain Network Analysis And Prediction: An Eeg Study On Exercise-induced Muscle Fatigue

PURPOSE: Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and the tolerance of fatigue has a large variability among subjects. The present study has been designed to find an effective way for predicting muscle fatigue and probing the neural mechanism of fatigue by a resting-state EEG network. METHODS: In this study, thirteen elite athletes (female) were enrolled to take part in an elbow flexion and extension task. Five-minute before- and after-fatigue-exercise resting-state EEG and fatiguing task electromyography (EMG) data were recorded. Based on the graph theory, we constructed the resting-state EEG network, and compared the network differences before- and after-task. To further validate, the correlation between the before-fatigue resting-state EEG network properties and the EMG-related fatigue indexes Mean power frequency(MPF) during exercise was profiled. Finally, a prediction model based on the before-fatigue resting-state EEG network property, including Clustering coefficient (CC), local efficiency (Le), global efficiency (Ge), and characteristic path length (CPL), was established to predict the EMG related fatigue indexes after fatigue. RESULTS: Firstly, the results of this study demonstrated a significant relationship between the resting-state brain network and muscle fatigue during exercise(P < 0.05). Secondly, we also validate that a significant relationship between resting-state brain network properties and MPF (P < 0.05). Finally, the study proved that the before-fatigue resting-state EEG network single property could predict the fatigue tolerance for individual subject accurately (CC: r = 0.651, P < 0.05; CPL: r = 0.592, P < 0.05; Ge: r = 0.603, P < 0.05; Le: r = 0.654, P < 0.05). CONCLUSION: In all, the resting-state brain network may provide indicators for the monitoring and prediction of muscle fatigue in athletes, in the meantime, provide biological markers for recognition and regulation of muscle fatigue based on BCI.

Spatial Navigation and Its Association With Biomarkers and Future Dementia in Memory Clinic Patients Without Dementia.

Impaired spatial navigation is considered an early sign in many neurodegenerative diseases. We aimed to determine whether spatial navigation was associated with future dementia in patients with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) and to explore associations between spatial navigation and biomarkers of Alzheimer disease (AD) and neurodegeneration. This study included memory clinic patients without dementia in the longitudinal BioFINDER cohort. The Floor Maze Test (FMT) was used to assess spatial navigation at baseline. Conversion to dementia was evaluated at 2-year and 4-year follow-ups. At baseline, amyloid-β 42/40 ratio, phosphorylated-tau (P-tau), and neurofilament light (NfL) were analyzed in CSF. Cortical thickness and volume of regions relevant for navigation and white matter lesion volume were quantified from MRI. The predictive role of the FMT for conversion to all-cause dementia was analyzed using logistic regression analyses in 2 models: (1) controlled for age, sex, and education and (2) adding baseline cognitive status and MMSE. Associations between FMT and biomarkers were adjusted for age, sex, and cognitive status (SCD or MCI). One hundred fifty-six patients with SCD and 176 patients with MCI were included. FMT total time was associated with progression to all-cause dementia in model 2 at 2-year (OR 1.10, 95% CI 1.04-1.16) and at 4-year follow-up (OR 1.10, 95% CI 1.04-1.16), i.e., a 10% increase in odds of developing dementia per every 10 seconds increase in FMT. In the adjusted analyses, P-tau and NfL were associated with FMT total time, as well as hippocampal volume, parahippocampal, and inferior parietal cortical thickness. Amyloid-β 42/40 ratio was not associated with FMT total time. Impaired spatial navigation was associated with conversion to dementia within 2 and 4 years and with key CSF and MRI biomarkers for AD and neurodegeneration in patients with SCD and MCI. This supports its use in early cognitive assessments, but the predictive accuracy should be validated in other cohorts. This is a Class I prospective cohort study demonstrating association of baseline markers of spatial recognition with development of dementia in patients with SCD or MCI at baseline.

A Computational Probe into the Behavioral and Neural Markers of Atypical Facial Emotion Processing in Autism.

Despite ample behavioral evidence of atypical facial emotion processing in individuals with autism spectrum disorder (ASD), the neural underpinnings of such behavioral heterogeneities remain unclear. Here, I have used brain-tissue mapped artificial neural network (ANN) models of primate vision to probe candidate neural and behavior markers of atypical facial emotion recognition in ASD at an image-by-image level. Interestingly, the image-level behavioral patterns of the ANNs better matched the neurotypical subjects 'behavior than those measured in ASD. This behavioral mismatch was most remarkable when the ANN behavior was decoded from units that correspond to the primate inferior temporal (IT) cortex. ANN-IT responses also explained a significant fraction of the image-level behavioral predictivity associated with neural activity in the human amygdala (from epileptic patients without ASD), strongly suggesting that the previously reported facial emotion intensity encodes in the human amygdala could be primarily driven by projections from the IT cortex. In sum, these results identify primate IT activity as a candidate neural marker and demonstrate how ANN models of vision can be used to generate neural circuit-level hypotheses and guide future human and nonhuman primate studies in autism.SIGNIFICANCE STATEMENT Moving beyond standard parametric approaches that predict behavior with high-level categorical descriptors of a stimulus (e.g., level of happiness/fear in a face image), in this study, I demonstrate how an image-level probe, using current deep-learning-based ANN models, allows identification of more diagnostic stimuli for autism spectrum disorder enabling the design of more powerful experiments. This study predicts that IT cortex activity is a key candidate neural marker of atypical facial emotion processing in people with ASD. Importantly, the results strongly suggest that ASD-related atypical facial emotion intensity encodes in the human amygdala could be primarily driven by projections from the IT cortex.

A novel self-aggregated gold nanoparticles based on sensitive immunochromatographic assays for highly detection of opium poppy in herbal teas

Opium poppy abused in food has aroused public concerns due to its serious side effects. Effective monitoring is essential to fight the abuse crisis. Herein, we synthesized an easily prepared, affordable, accessible highly aggregated gold nanoparticles (AGNPs) performing in lateral flow immunoassay (LFIA) for detection opium poppy in herbal teas. Simultaneously, a LFIA based ontime-resolved fluorescent microspheres (TRFMs) was developed as contrastive method. In this study, morphine (MOR), codeine (COD) and thebaine (THE) were as the specific recognition markers of opium poppy. Results demonstrated the quantitative limits of detection were 0.0049/0.0053/0.084, 0.034/0.037/0.37 ng mL−1 for AGNPs/TRFMs-LFIA, respectively. The recoveries were 95%-107.5%/91%-106.7% with coefficient of variation was 1.6%-6.6%/1.8%-7.2%, indicating excellent accuracy and precision. Parallel experiments among AGNPs/TRFMs-LFIA and LC-MS/MS analysis showed good correlation. Overall, AGNPs-LFIA executed quantitative analysis within 15 min on the basis of simple treatment while providing a rapid and sensitive analysis strategy for illegal drugs abused.

Non-monotonic developmental trend of holistic processing in visual expertise: the case of Chinese character recognition

Holistic processing has been identified as an expertise marker of face and object recognition. By contrast, reduced holistic processing is purportedly an expertise marker in recognising orthographic characters in Chinese. Does holistic processing increase or decrease in expertise development? Is orthographic recognition a domain-specific exception to all other kinds of recognition (e.g. face and objects)? In two studies, we examined the developmental trend of holistic processing in Chinese character recognition in Chinese and non-Chinese children, and its relationship with literacy abilities: Chinese first graders—with emergent Chinese literacy acquired in kindergarten—showed increased holistic processing perhaps as an inchoate expertise marker when compared with kindergartners and non-Chinese first graders; however, the holistic processing effect was reduced in higher-grade Chinese children. These results suggest a non-monotonic inverted U-shape trend of holistic processing in visual expertise development: An increase in holistic processing due to initial reading experience followed by a decrease in holistic processing due to literacy enhancement. This result marks the development of holistic and analytic processing skills, both of which can be essential for mastering visual recognition. This study is the first to investigate the developmental trend of holistic processing in Chinese character recognition using the composite paradigm.

Key point localization and recurrent neural network based water meter reading recognition

Due to the complicated arrangement of the pipes in the narrow space leads to random orientation of the mechanical water meter dial meanwhile its digit wheels are accompanied by arbitrary angle rotation, which makes the detection and recognition of meter reading more difficult. Even the latest visual network technology cannot deal with the challenges. In this paper, two special visual task networks are being closely cooperated to solve above issues. First, a professional water meter detection method is proposed by redesigning and retraining a human joints detection network to accurately locate four key points of reading region. Based on key points the distorted reading region will be geometric corrected by using homography relation to reduce the interference from shooting angle and improve accuracy of subsequent digit recognition. Then, a water meter reading recognition method is proposed by modifying a recurrent block convolutional network. The robustness of digit recognition is improved by block recognition and transcription of reading region features. During transcription stage, we add new recognition markers and probability vectors between each digit in dictionary to solve the issue of digit wheels rotations. Finally, our method achieves more robust water meter detection in harsh environment and higher recognition accuracy. Experimental results showed that our method can get better performance in detection efficiency (6.15 fps) and accuracy (95.30%) compared with recent related works and closer to the level of practical application.

Neural Mechanisms of Facial Emotion Recognition in Autism: Distinct Roles for Anterior Cingulate and dlPFC

ABSTRACT Objective The present study sought to measure and internally validate neural markers of facial emotion recognition (FER) in adolescents and young adults with ASD to inform targeted intervention. Method We utilized fMRI to measure patterns of brain activity among individuals with ASD (N = 21) and matched controls (CON; N = 20) 2 s prior to judgments about the identity of six distinct facial emotions (happy, sad, angry, surprised, fearful, disgust). Results Predictive modeling of fMRI data (support vector classification; SVC) identified mechanistic roles for brain regions that forecasted correct and incorrect identification of facial emotion as well as sources of errors over these decisions. BOLD signal activation in bilateral insula, anterior cingulate (ACC) and right dorsolateral prefrontal cortex (dlPFC) preceded accurate FER in both controls and ASD. Predictive modeling utilizing SVC confirmed the utility of ACC in forecasting correct decisions in controls but not ASD, and further indicated that a region within the right dlPFC was the source of a type 1 error signal in ASD (i.e. neural marker reflecting an impending correct judgment followed by an incorrect behavioral response) approximately two seconds prior to emotion judgments during fMRI. Conclusions ACC forecasted correct decisions only among control participants. Right dlPFC was the source of a false-positive signal immediately prior to an error about the nature of a facial emotion in adolescents and young adults with ASD, potentially consistent with prior work indicating that dlPFC may play a role in attention to and regulation of emotional experience.

Hypoxic Ischemic Encephalopathy (HIE) in Term and Preterm Infants.

Hypoxic-ischemic syndrome (HIS) and Hypoxic-ischemic encephalopathy (HIE) are conditions that affect term and premature babies, with different pathophysiology and different brain disorders. HIE appears in 1-6 / 1000 live births and 26/1000 live births in developing countries. 15-20% die in the early neonatal period, while surviving babies have severe neurological impairment, including cerebral palsy, epilepsy, visual and hearing impairment, cognitive impairment, intellectual, behavioural, and social disorders. The hypoxic-ischemic event occurs before, during or after birth. The reasons may be related to the mother, the way of birth, the placenta, and the newborn. The criteria for diagnosis of HIE include a combination of perinatal factors, the need for resuscitation, standard neurological examinations, neurophysiological monitoring, neuroimaging methods and biochemical markers. The most effective treatment for HIE is hypothermia in combination with pharmacological therapy. HIE and HIS are problem that still persist in developing countries due to inadequate obstetric care, neonatal resuscitation, and hypothermia. Current and emerging research for HIE examines new markers for early recognition, treatment, and appropriate neuroprotection of high-risk term and premature infants.

Research on depression recognition based on brain function network

Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of "small world" and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.

Decreased Netrin-1 in Mild Cognitive Impairment and Alzheimer's Disease Patients.

Background and ObjectiveInflammatory mediators are closely associated with the pathogenesis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Netrin-1 is an axon guidance protein and despite its capacity to function as a neuroimmune guidance signal, its role in AD or MCI is poorly understood. In addition, the association among netrin-1, cognitive impairment and serum inflammatory cytokines such as interleukin-17 (IL-17) and tumor necrosis (TNF-α) remains unclear. The aim of this study was to determine serum levels of IL-17, TNF-α and netrin-1in a cohort of AD and MCI patients, and to study the relationship between these cytokines and cognitive status, as well as to assess the possible relationships between netrin-1 levels and inflammatory molecules.MethodsSerum concentrations of netrin-1, TNF-α and IL-17 were determined in 20 AD patients, 22 MCI patients and 22 healthy controls using an enzyme-linked immunosorbent assay (ELISA). In addition, neuropsychological evaluations and psychometric assessments were performed in all subjects.ResultsSerum netrin-1 levels were decreased in AD and MCI patients and were positively correlated with Mini Mental State Examination (MMSE) scores. In contrast, serum TNF-α and IL-17 levels were elevated in AD and MCI cohorts and negatively correlated with MMSE scores. Serum netrin-1 levels were inversely related with TNF-α and IL-17 levels in AD, but not MCI, patients.ConclusionBased on the findings reported here, netrin-1 may serve as a marker for the early recognition of dementia and predict cognitive impairment.

Pragmatic Applications and Universality of DNA Barcoding for Substantial Organisms at Species Level: A Review to Explore a Way Forward.

DNA barcodes are regarded as hereditary succession codes that serve as a recognition marker to address several queries relating to the identification, classification, community ecology, and evolution of certain functional traits in organisms. The mitochondrial cytochrome c oxidase 1 (CO1) gene as a DNA barcode is highly efficient for discriminating vertebrate and invertebrate animal species. Similarly, different specific markers are used for other organisms, including ribulose bisphosphate carboxylase (rbcL), maturase kinase (matK), transfer RNA-H and photosystem II D1-ApbsArabidopsis thaliana (trnH-psbA), and internal transcribed spacer (ITS) for plant species; 16S ribosomal RNA (16S rRNA), elongation factor Tu gene (Tuf gene), and chaperonin for bacterial strains; and nuclear ITS for fungal strains. Nevertheless, the taxon coverage of reference sequences is far from complete for genus or species-level identification. Applying the next-generation sequencing approach to the parallel acquisition of DNA barcode sequences could greatly expand the potential for library preparation or accurate identification in biodiversity research. Overall, this review articulates on the DNA barcoding technology as applied to different organisms, its universality, applicability, and innovative approach to handling DNA-based species identification.