Novel Sequencing and Genomic Technologies Revolutionized Rice Genomic Study and Breeding

Rice is one of the most important food crops worldwide. Population growth and climate change posed great challenges for further rice production. In the past decade, we have witnessed an explosive development in novel sequencing and genomic technologies. These technologies have been widely applied in rice genomic study and improvement processes, and contributed greatly to increase the efficiency and accuracy of rice breeding. On the other hand, novel sequencing and genomic technologies also promote the shift of breeding schemes from conventional field selection processes to genomic assisted breeding. These technologies have revolutionized almost every aspect of rice study and breeding. Here, we systematically sorted out and reviewed the progress and advancements of sequencing and genomic technologies. We further discussed how these technologies were incorporated into rice breeding practices and helped accelerate the rice improvement process. Finally, we reflected on how to further utilize novel sequencing and genomic technologies in rice genetic improvement, as well as the future trends of advancement for these technologies. It can be expected that, as the sequencing and genomic technologies will develop much more quickly in the future, and be combined with novel bioinformatics tools, rice breeding will move forward into the genomic assisted era.

Cyberchondria During the COVID-19 Pandemic

Many factors govern the behavior pertaining to anxiety regarding health – it may be stressors, triggers, adverse situations faced by people. Since the outbreak of the COVID-19 virus, health-related anxiety has been widespread. December 2019 saw the occurrence of a form of pneumonia, not known at that time, from the Wuhan region of China to rapidly proceed into a worldwide pandemic and lockdown. This disrupted normal life across the globe and affected social and economic functioning. Cyberchondria has been one of the coping strategies seen during this time, pertaining to online health-related searches which offer answers and relief as seen at the surface but also bringing a plethora of mental health challenges. The authors in this chapter try to simplify what exactly cyberchondria is and how has it affected the lives of the worldwide population during the COVID-19 crisis, the biggest pandemic of recent times. The origin, development, and prevalence of the concept, along with coping, treatment, and prevention of the condition are discussed.

Ten food that boost Immune System: A Review

The immunity and system functions to fight against infections square measure considerably wedged by inappropriate food and nutrition. Long run deficiency disease is universally thought-about because the leading explanation for system deficiency. A considerable proportion of the worldwide population doesn't meet the counselled daily intake of nutrient. The COVID-19 pandemic has targeted attention on the role of the system, with health scientists and nutritionists urging folks to require supplements and/or eat specific foods (nutrients) to super-charge their immune systems. The system is that the most complicated system of physical structure. For this we have a tendency to needed the supplements like food for maintenance of system.in this article here could be a discussion of foods like Citrus fruits like lemon, orange, lime etc., broccoli, spinach, turmeric, yoghurt, kiwi, garlic, papaya, almonds, ginger, tulsi, etc things helps in boosting our immunity to fight against infections. In this report, foods that helps in maintain system or immunity boosting material is mentioned.

Can SARS-CoV-2 Infection Lead to Neurodegeneration and Parkinson’s Disease?

The SARS-CoV-2 pandemic has affected the daily life of the worldwide population since 2020. Links between the newly discovered viral infection and the pathogenesis of neurodegenerative diseases have been investigated in different studies. This review aims to summarize the literature concerning COVID-19 and Parkinson’s disease (PD) to give an overview on the interface between viral infection and neurodegeneration with regard to this current topic. We will highlight SARS-CoV-2 neurotropism, neuropathology and the suspected pathophysiological links between the infection and neurodegeneration as well as the psychosocial impact of the pandemic on patients with PD. Some evidence discussed in this review suggests that the SARS-CoV-2 pandemic might be followed by a higher incidence of neurodegenerative diseases in the future. However, the data generated so far are not sufficient to confirm that COVID-19 can trigger or accelerate neurodegenerative diseases.

Post- COVID-19 multisystem inflammatory syndrome in children

COVID-19 is a severe acute respiratory infection affecting worldwide population. There are many cases of complications after the COVID exposure occurring nowadays. One among is Post-COVID-19 Multisystem Inflammatory Syndrome in Children (MIS-C). As per CDC report till March 1, 2021, 2617 cases of MIS-C were meeting the definite case criteria and among 33 death cases were reported. Here we report a case of COVID-19 associated Multi-system inflammatory syndrome in a child (MIS-C) interpreted with WHO case definition criteria. The patient was a 7-year-old boy, with initial presentation of moderate fever, non-itchy red blanching rashes, breathlessness, later progressed to cardiogenic shock accompanied by positive SARS-CoV-2 antigen result. The emergency cardiogenic shock treatment protocol was followed with initial stabilization and resuscitation strategy. He was successfully managed by three days of IV Immunoglobulin 2g/kgand Methylprednisolone 2mg/kg/day therapy along with other supportive treatments. The patient was discharged after 20 days of hospital stay with improved health condition. Our case report will strengthen the exposure-outcome relations between the coronavirus infection and MIS-C, moreover the strategies carried out in our case will be a future direction for the effective management of MIS-C.

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

<div>Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a patient-dependent process which is crucial for the treatment selection process. The selection of the proper treatment relies on the correct identification of seizures type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model.</div>

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

<div>Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a patient-dependent process which is crucial for the treatment selection process. The selection of the proper treatment relies on the correct identification of seizures type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model.</div>

Impact of Comorbidities and COVID-19 on the Survival Rate of Geriatric Patients Receiving Anesthesia Services: A Prospective Cohort Study in Indonesia

Background As life expectancy increases, the worldwide population aged 60 years and older increases year by year. Consequently, more older people receive medical attention, especially those who undergo surgery. In addition, the COVID-19 pandemic has had a global impact on elderly patients, especially those undergoing surgery. This study aims to describe the characteristics and analyze the survival rate of elderly patients who receive anesthesia services, especially those with comorbidities and COVID-19. Methods A prospective cohort study at 14 central hospitals in Indonesia analyzed 1621 elderly patients (67.1 ± 6.2 years old). The variables that were recorded included patient characteristics, comorbidities, the COVID-19 status, and the survival rate, including 30-day mortality. Results The 30-day mortality was 4.4%. The most comorbidity was hypertension (30.0). Patients with a Charlson's Comorbidity Index Score of 3-4 had a higher death rate (15.3%). The highest mortality rates were in the patients who had dementia, rheumatologic disease, liver disease, previous myocardial infarction, and diabetes with chronic complications as comorbidities. The percent of patients with COVID-19 who died was 26.6%. Patients with several comorbidities and COVID-19 had a lower survival rate than those without (log-rank p<0.05) Conclusion Approximately four in ten elderly patients receiving anesthesia died, and the percent increased when the patients had comorbidities and COVID-19.

Affective Neuroscience Contributions to the Treatment of Addiction: The Role of Social Instincts, Pleasure and SEEKING

Addiction is an illness prevalent in the worldwide population that entails multiple health risks. Because of the nature of addictive disorders, users of drugs seldom look for treatment and when they do, availability can be difficult to access. Permanence in treatment and its outcomes vary from case to case. Most models work from a multidisciplinary approach that tackles several dimensions of addictive disorders. However, the different etiological factors claim for a personalized treatment to enhance opportunities for better results. Problems in relationships with others play an important role in the etiology and the recovery process of addiction. This paper focuses on the social-environmental causes of addiction based on an affective neuroscience approach that attempts to integrate the interplay between social instincts, pleasure, and the SEEKING system in addiction. To advance toward better treatment strategies, it is pertinent to understand the limitations of the current multidisciplinary models. Acknowledging the social nature of the human brain may help to identify the quality of different types of traumatic early life experiences in drug users and how to address them in what may become a neuropsychoanalytic treatment of addiction.

Performance of the Car Carrier Shipping Sector in the Iberian Peninsula under the COVID-19 Scenario

The Iberian Peninsula represents the second European producer and the eighth world producer of vehicles in 2020. The pandemic of SARS-Cov2 introduced severe challenges for the worldwide population and for the industrial production and supply chains. The car carrier shipping sector has not been studied in depth in the Maritime Transportation and Port Logistics literature. This research pays special attention to the performance of this traffic in the Iberian Peninsula in the pre-pandemic era and under COVID-19 pandemic conditions, in which seven ports with car-carrier ship traffic in the Iberian Peninsula are analyzed. First, a dynamic portfolio analysis about how the COVID-19 pandemic affected the evolution of competitive positions of Iberian Peninsula ports is performed. Second, studies of the seasonality patterns of vehicle movements in ports of the Iberian Peninsula were carried out using time series of the periods from 2012 to 2019 and from 2012 to 2020. The Seasonal Variation Index (SVI) was employed to determine the seasonality of vehicle traffic in the periods considered and analyses were performed independently for both embarking and disembarking traffic. Important conclusions are revealed, e.g., during a year of COVID-19, the seven ports had decreased vehicle movements for disembarking traffic and only one port increased the traffic for embarking traffic. Furthermore, COVID-19 introduced important changes in the seasonality patterns of vehicle movements during the first months of the pandemic.