Mirogabalin Decreases Pain-like Behaviours and Improves Opioid and Ketamine Antinociception in a Mouse Model of Neuropathic Pain

Neuropathic pain remains a difficult clinical challenge due to its diverse aetiology and complex pathomechanisms, which are yet to be fully understood. Despite the variety of available therapies, many patients suffer from ineffective pain relief; hence, the search for more efficacious treatments continues. The new gabapentinoid, mirogabalin has recently been approved for clinical use. Although its main mechanism of action occurs at the α2σ-1 and α2σ-2 subunits of calcium channels and is well documented, how the drug affects the disturbed neuropathic interactions at the spinal cord level has not been clarified, which is crucial information from a clinical perspective. The findings of our study suggest that several indirect mechanisms may be responsible for the beneficial analgesic effect of mirogabalin. This is the first study to report that mirogabalin enhances the mRNA expression of spinal antinociceptive factors, such as IL-10 and IL-18BP, and reduces the concentration of the pronociceptive substance P. Importantly, mirogabalin improves the morphine-, buprenorphine-, oxycodone-, and ketamine-induced antinociceptive effects in a neuropathic pain model. Our findings support the hypothesis that enhancing opioid and ketamine analgesia by combining these drugs with mirogabalin may represent a new strategy for the effective pharmacotherapy of neuropathic pain.

Distance-Entropy: An Effective Indicator for Selecting Informative Data

Smart agriculture is inseparable from data gathering, analysis, and utilization. A high-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, while ignoring the crucial information aspect. In this work, taking the crop pest recognition task as an example, we proposed an effective indicator of distance-entropy to distinguish the good and bad data from the perspective of information. Many comparative experiments, considering the mapping feature dimensions and base data sizes, were conducted to testify the validity and robustness of this indicator. Both the numerical and the visual results demonstrate the effectiveness and stability of the proposed distance-entropy method. In general, this study is a relatively cutting-edge work in smart agriculture, which calls for attention to the quality assessment of the data information and provides some inspiration for the subsequent research on data mining, as well as for the dataset optimization for practical applications.

Meal-Monitoring Systems Using Weight and Temperature Sensors for Elder Residents in Long-Term Care Facilities

This paper presents a few meal-monitoring systems for elder residents (especially patients) in LTCFs by using electronic weight and temperature sensors. These monitoring systems enable to convey the information of the amount of meal taken by the patients in real-time via wireless communication networks onto the mobile phones of their nurses in charge or families. Thereby, the nurses can easily spot the most patients who need immediate assistance, while the families can have relief in seeing the crucial information for the well-being of their parents at least three times a day. Meanwhile, the patients tend to suffer burns of their tongues because they can hardly recognize the temperature of hot meals served. This situation can be avoided by utilizing the meal temperature-monitoring system, which displays an alarm to the patients when the meal temperature is above the reference. These meal-monitoring systems can be easily implemented by utilizing low-cost sensor chips and Arduino NANO boards so that elder-care hospitals and nursing homes can afford to exploit them with no additional cost. Hence, we believe that the proposed monitoring systems would be a potential solution to provide a great help and relief for the professional nurses working in elder-care hospitals and nursing homes.

Comparative geomorphometric approach to understand the hydrological behaviour and identification of the Erosion prone areas of a coastal watershed using RS and GIS tools

Assessment of the geomorphometric parameters using Remote Sensing (RS) and Geographic Information System (GIS) tools forms an important part in routing the runoff and other hydrological processes. The current study uses a geospatial model based on geomorphometric parameters for the categorization of surface runoff and identification of the erosion-prone areas in the watershed of the Kuttiyadi River. The 4th order Kuttiyadi river is dominated by a dendritic to semi-dendritic drainage pattern in the subwatersheds. The linear aspect of the subwatersheds indicates towards the presence of permeable surface and subsurface materials with uniform lithology. The aerial and relief aspects of the subwatersheds shows fine drainage texture, gentle slopes, delayed peak flow, flatter hydrograph, and large concentration time which shows that subwatersheds are quite capable of managing flash floods during storm events. The estimated values of surface runoff (Q) and sediment production rate (SPR) are range from 2.13 to 32.88 km2-cm/km2 and 0.0004–0.017 Ha-m/100km2/year respectively and suggest that Subwatershed 1 (SW1) will generate more surface runoff and is prone to soil erosion followed by subwatershed 2 (SW2) in comparison to other subwatersheds. This paper aims to fill the knowledge gap regarding categorization of flow and erosion dynamics in a coastal river watershed. We believe that our work may work help in providing the crucial information for decision-makers and policymakers responsible for establishing suitable policies and sustainable land use practices for the watershed.

GNNQQNY: Methodology for biophysical and structural understanding of aggregation

GNNQQNY sequence offers crucial information about the formation and structure of an amyloid fibril. In this study, we demonstrate a reproducible solubilisation protocol where the reduction of pH to 2.0 resulted in the generation of GNNQQNY monomers. The subsequent ultracentrifugation step removes the residual insoluble peptide from the homogeneous solution. This procedure ensures and allows the peptides to remain monomers till their aggregation is triggered by adjusting the pH to 7.2. The aggregation kinetics analysis showed a distinct lag-phase that is concentration-dependent, indicating nucleation-dependent aggregation kinetics. Nucleation kinetics analysis suggested a critical nucleus of size ~7 monomers at physiological conditions. The formed nucleus acts as a template for further self-assembly leading to the formation of highly ordered amyloid fibrils. These findings suggest that the proposed solubilisation protocol provides the basis for understanding the kinetics and thermodynamics of amyloid nucleation and elongation in GNNQQNY sequences. This procedure can also be used for solubilising such small amyloidogenic sequences for their biophysical studies.

Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)

Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.

Multiproduct manufacturer-retailer coordinated supply chain with adjustable rate for common parts, delayed differentiation, and multi-shipment

Operating in today’s turbulent and competitive world marketplaces, manufacturers must find the best production scheme and delivery policy to meet timely client’s multiproduct requirements and minimize the total manufacturing-shipment expenses. This study proposes a two-stage delayed differentiation model for a multiproduct manufacturer-retailer coordinated supply chain featuring the adjustable-rate for making common parts and a multi-shipment policy for transporting finished goods. The aim is to help present-day manufacturers achieve their operational goals mentioned above. The mathematical techniques help us build a specific model to explicitly represent the problem and derive its overall operating expense. Then, the convexity of the total expense is verified by Hessian matrix equations. The differential calculus helps derive the cost-minimized fabrication-shipment decision. This study offers an example to demonstrate the applicability and capabilities of our proposed model numerically. The following crucial information has been made available to the managers to facilitate their operating decision makings: (1) the problem’s best fabrication-shipment policy; (2) the collective influence of various common part’s completion rates and values on the problem’s total expenses and optimal fabrication-shipment policy; (3) the impact of various adjustable-rates in stage one on utilization and stage one’s uptime; (4) the details of cost contributors to the problem; and (5) the collective impacts of critical features on the problem’s performance.

Imaging the Water Snowline around Protostars with Water and HCO+ Isotopologues

The water snowline location in protostellar envelopes provides crucial information about the thermal structure and the mass accretion process as it can inform about the occurrence of recent (≲1000 yr) accretion bursts. In addition, the ability to image water emission makes these sources excellent laboratories to test indirect snowline tracers such as H13CO+. We study the water snowline in five protostellar envelopes in Perseus using a suite of molecular-line observations taken with the Atacama Large Millimeter/submillimeter Array (ALMA) at ∼0.″2−0.″7 (60–210 au) resolution. B1-c provides a textbook example of compact H 2 18 O (31,3−22,0) and HDO (31,2−22,1) emission surrounded by a ring of H13CO+ (J = 2−1) and HC18O+ (J = 3−2). Compact HDO surrounded by H13CO+ is also detected toward B1-bS. The optically thick main isotopologue HCO+ is not suited to trace the snowline, and HC18O+ is a better tracer than H13CO+ due to a lower contribution from the outer envelope. However, because a detailed analysis is needed to derive a snowline location from H13CO+ or HC18O+ emission, their true value as a snowline tracer will lie in the application in sources where water cannot be readily detected. For protostellar envelopes, the most straightforward way to locate the water snowline is through observations of H 2 18 O or HDO. Including all subarcsecond-resolution water observations from the literature, we derive an average burst interval of ∼10,000 yr, but high-resolution water observations of a larger number of protostars are required to better constrain the burst frequency.

Benefits and Challenges of Online Teaching During the COVID-19 Pandemic at Rundu Campus of the University of Namibia

This paper presents the benefits and challenges of online teaching during the COVID-19 pandemic experienced by educators at the Rundu Campus of the University of Namibia. Researchers used a structured interview guide to collect data from 14 conveniently selected lecturers from a population of 65. Findings of the study indicate that the benefits of using online teaching and learning include flexibility, ability to teach large classes; increased interaction and engagement between lecturers and students; and increased learning opportunities for lecturers. The study further found that some of the significant challenges lecturers experienced with online teaching and learning include lack of information and technology skills, internet connectivity and availability; poor student attendance; and loneliness. The study provided crucial information on lecturers' progress within the framework of online teaching and learning mode. The paper recommends that lecturers receive formal training on online teaching and learning tools to minimise the limitations. The study also suggests increased psychosocial support for lecturers to curb feelings of isolation and loneness during this time.

Smart Fish Feeder Using Solar Energy

Smart fish feeder is an emerging concept of the current trend which use Internet of Things (IoT) technology to operate, monitoring and provides crucial information and status to the whole farming system. This project aims to provide such essential proof of concept that utilized IoT technology combine with the solar energy to power up servo motor and temperature sensor that connect from NodeMCU for the agriculture system. The main objectives of this project are specifically focused on the development of a smart fish feeder by using the solar system with charging capability and controlled by the IoT system. Such a fish feeder system was powered up by 12V battery using 10W solar panel controlled by a solar charger controller. The solar energy was stored in 12 V rechargeable battery. IoT-controlled sensors were also attached to the fish feeder system for providing essential information on temperature and fish feeder timer via the Blynk platform. The results of the developed system successfully proved the concept is workable and could be extended to a larger scale of the farming industry. Owing to its energy autonomy and low cost, the system has the potential to be useful in smart farming technology.