Maneuvering humidity to augment response of phosphine against adult and egg stages of Liposcelis bostrychophila Badonnel (Psocoptera: Liposcelidae)

No Abstrac

Performance of taste enhancers mixed with cereal bases and evaluation of the most preferred bait composition for Bandicota bengalensis (Gray)

Baiting technique if appropriately applied is the most reliable strategy to control rodent pests. Behavior modifying components may play a significant role in developing the most attractive baits. An attempt was therefore made to investigate the behavior revolutionizing effect of taste enhancers including peanut oil, peanut butter, egg shell and fishmeal, on exploratory approaches of rodents. Precise role of additives and impact of particle size of cereal bases has been discussed aiming to minimize bait shyness, neophobia and development of the most preferred bait combination for effective control of bandicoot rat Bandicota bengalensis.Key words: Bandicota bengalensis, cereals baits, taste enhancers, shyness, neophobia

Germination of Themeda triandra (Kangaroo grass) as affected by different environmental conditions and storage periods

Low rainfall in range areas restricts germination, growth and development of majority of range grasses. However, germination and establishment potential of forage grasses vary and depends on environmental conditions. Themeda triandra is an excellent known grass to grow under different environmental conditions. T. triandra naturally grows over an extensive geographical range on many soil types. Germination of T. triandra is the key factor in its establishment or re-establishment because its germination varies widely which is also affected by storage periods of seed. Germination response of Themeda to storage period was conducted in the laboratory. Four storage periods (Fresh seed, 6, 12 and 18 months old) seeds were sown in laboratory in germination trays placed in growth chamber in completely randomized design. Germination was counted till 40 days after sowing and percentage calculated thereafter. The 12 month old seeds gave the maximum 84% germination. On the basis of laboratory experiment, 12 months seeds were sown at 3 diverse locations (Rawalpindi, Jhelum and Talagang) with 4 spacing treatments (20, 30, 40 and Broadcast) in completely randomized block design. Germination was recorded for 40 days after sowing (DAS) and the maximum germination was observed in 25 - 30 DAS depending on the environmental conditions of experimental sites. Closer plant spacing (20 cm) gave the maximum (79%) germination at high rainfall area (Rawalpindi) while the least (52%) was recorded for the wider plant spacing at low rain fall area (Talagang)

Comparative Study for Assessment of Koha and SLIMS Features in Public Sector College Libraries of Sindh

The core aim of the study was to assess KOHA and SLiMS features in Government College Libraries of Sindh. The objectives of the study were (a) to explore the reasons of Koha and SLiMS adoption and (b) to recognize the problems face by library professionals in the implementation of Koha and SLiMS. To meet the objectives, the study used a quantitative research approach and the survey method based on the questionnaire. Purposive sampling technique was used. The data was gathered only from those librarians who were working in Govt Colleges of Karachi, Hyderabad, and Sukkur and were using Koha or SLiM software for the purpose of library automation. The response was received from 73 librarians out of 97. This study found that libraries were adopting Koha because Koha provided search facility for copy cataloguing through Z39.50, Koha provides multilingual support. It has popularity among professional community. It provides MARC21 standard for cataloguing and provision of discovery features. This study discovered that libraries adopted SLiMS due to availability of Web OPAC, MARC21 standard for cataloguing, multilingual support, and search facility for copy cataloguing through Z39.50. The study highlighted that library professionals encountered problems during the implementation of Koha included shortage of skilled manpower to install and maintain the software, shortage of finance for hardware requirement, and up-gradation of Koha versions development of software. The present study reported that requirement of highly networked and integrated environment, up gradation of SLiMS versions, support issues of UTF-8 languages, and shortage of finance for hardware requirement were the major problems in the implementation of SLiMS software

Outcome of Percutaneous Ultrasound Guided Aspiration versus Open Surgical Drainage of Psoas Muscle Abscess

Objective: To compare the outcome of percutaneous ultrasound guided aspiration V/S open surgical drainage for psoas muscle abscess. Methodology: This comparative study was conducted in department of general surgery at Liaquat medical university hospital Hyderabad/Jamshoro, from June 2017 to November 2017. Diagnosed Patients of psoas muscle abscess size more than 5cm, between 18 to 60 years of age and either of gender were included. Patients were randomly divided into two groups, A and B by odd and even method, patients in group A abscess was aspirated by percutaneous ultrasound guided aspiration and patients in group B was underwent open surgical drainage, all the data were entered in the pre designed performa and analyzed into SPSS V:16.0 Results: A total of 58 patients of Psoas muscle abscess were selected, the mean age of study subjects of group A was 38.5+10.5 and group B was 36.5+12.7 (p-673). Early post-operative pain relief was assessed among patients of group A as compared to group B. As per outcome resolution of abscess cavity was significantly high among patients of group B (p-0.004), while post-operative Hospital stay was significantly lower in group A (p-0.002). Conclusion: Both techniques has their own benefits like percutaneous aspiration has shorter duration of hospital stay while in complete resolution of abscess cavity was found in open surgical drainage group of patients

Predicting Poor Outcomes Among Individuals Seeking Care for Major Depressive Disorder

OBJECTIVE: To develop and validate algorithms to identify individuals with major depressive disorder (MDD) at elevated risk for suicidality or for an acute care event. METHODS: We conducted a retrospective cohort analysis among adults with MDD diagnosed between January 1, 2018 and February 28, 2019. Generalized estimating equation models were developed to predict emergency department (ED) visit, inpatient hospitalization, acute care visit (ED or inpatient), partial-day hospitalization, and suicidality in the year following diagnosis. Outcomes (per 1000 patients per month, PkPPM) were categorized as all-cause, psychiatric, or MDD-specific and combined into composite measures. Predictors included demographics, medical and pharmacy utilization, social determinants of health, and comorbid diagnoses as well as features indicative of clinically relevant changes in psychiatric health. Models were trained on data from 1.7M individuals, with sensitivity, positive predictive value, and area-under-the-curve (AUC) derived from a validation dataset of 0.7M. RESULTS: Event rates were 124.0 PkPPM (any outcome), 21.2 PkPPM (psychiatric utilization), and 7.6 PkPPM (suicidality). Among the composite models, the model predicting suicidality had the highest AUC (0.916) followed by any psychiatric acute care visit (0.891) and all-cause ED visit (0.790). Event-specific models all achieved an AUC \u3e0.87, with the highest AUC noted for partial-day hospitalization (AUC = 0.938). Select predictors of all three outcomes included younger age, Medicaid insurance, past psychiatric ED visits, past suicidal ideation, and alcohol use disorder diagnoses, among others. CONCLUSIONS: Analytical models derived from clinically-relevant features identify individuals with MDD at risk for poor outcomes and can be a practical tool for health care organizations to divert high-risk populations into comprehensive care models

Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches

OBJECTIVE: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state. METHODS: SCI data were collected from high-risk psychiatric inpatients (N = 591) grouped based on their short-term suicidal behavior, that is, those who attempted suicide between intake and 1-month follow-up dates (N = 20) and those who did not (N = 571). Data were analyzed using three predictive algorithms (logistic regression, random forest, and gradient boosting) and three sampling approaches (split sample, Synthetic minority oversampling technique, and enhanced bootstrap). RESULTS: The enhanced bootstrap approach considerably outperformed the other sampling approaches, with random forest (98.0% precision; 33.9% recall; 71.0% Area under the precision-recall curve [AUPRC]; and 87.8% Area under the receiver operating characteristic [AUROC]) and gradient boosting (94.0% precision; 48.9% recall; 70.5% AUPRC; and 89.4% AUROC) algorithms performing best in predicting positive cases of near-term suicidal behavior using this dataset. CONCLUSIONS: ML can be useful in analyzing data from psychometric scales, such as the SCI, and for predicting near-term suicidal behavior. However, in cases such as the current analysis where the data are highly imbalanced, the optimal method of measuring performance must be carefully considered and selected